Characterising the constituents for the House of Representative members in Victoria

ETC5512 Assignment 2, Master of Business Analytics

🔍 Analysis

Research Question 1

How many people lived in each electoral division of interest?

library(tidyverse)
library(sf)
vic_map <- read_sf(here::here("data/E_AUGFN3_region.shp")) %>% 
   # to match up with election data
   mutate(DivisionNm = toupper(Elect_div)) 
sa1_geomap <- read_sf(here::here("data/census2016_eiuwa_vic_short.gpkg"), 
                  layer = "census2016_eiuwa_vic_sa1_short") %>%
mutate(centroid = map(geom, st_centroid),
centroid = st_as_sfc(centroid, crs = st_crs(vic_map)),
which = as.integer(st_intersects(centroid, vic_map)),
Elect_div = ifelse(is.na(which),
"None",
vic_map$Elect_div[which])) %>%
select(sa1_7digitcode_2016, Elect_div)

sa1_digitcode1<-sa1_geomap%>% filter (Elect_div %in% c('Melbourne','Kooyong','Macnamara','Chisholm','Higgins','Goldstein','Hotham'))

sa1_digitcode1<- as_tibble(sa1_digitcode)

sa1_digitcode1<- sa1_digitcode1 %>% select(- geom)

census_path <- here::here("data/2016 Census GCP All Geographies for VIC/{geo}/VIC")

SA1_paths <- glue::glue(census_path, "/2016Census_G04{alpha}_VIC_SA1.csv", 
                        geo = "SA1", alpha = c("A", "B"))

clean_G04P <- function(df, id) {
   df %>% 
      pivot_longer(-{{id}},
                   names_to = "category",
                   values_to = "count") %>% 
      filter(str_detect(category, "P")& !str_detect(category,"Tot") & !str_detect(category, "0_4") & !str_detect(category, "5_9") & !str_detect(category, "10_14") & !str_detect(category, "15_19") & !str_detect(category, "20_24") &  !str_detect(category, "25_29") & !str_detect(category, "30_34") & !str_detect(category, "35_39") & !str_detect(category, "40_44") & !str_detect(category, "45_49") & !str_detect(category, "50_54") & !str_detect(category, "55_59") & !str_detect(category, "60_64") &!str_detect(category, "65_69") & !str_detect(category, "70_74") & !str_detect(category, "75_79"))%>%
      unglue_unnest(category, 
                   c("{}_{}_{Age=\\d+}_{}"), 
                   remove = FALSE)
}

SA1_G04tidytot_P <- map_dfr(SA1_paths, ~{
                              df <- read.csv(.x)
                              clean_G04P(df, SA1_7DIGITCODE_2016)
                           })

write_csv(SA1_G04tidytot_P,"data/SA1_G04tidytot_P.csv")

SA1_G04tidytot_P<- read_csv("data/SA1_G04tidytot_P.csv")

Aust_tot_pop<-SA1_G04tidytot_P%>%group_by(Elect_div)%>% summarise(Tot_P=sum(count))

#Table using Kableextra function

SA1_G04tidytot_P%>%group_by(Elect_div)%>% summarise(Tot_P=sum(count))%>%kbl(caption="Total Population In Each Electoral Division 2016",table.attr="style='width:70%;'") %>%
  kable_paper("hover",full_width = T,html_font = "Cambria", position= "left" )

Total Population In Each Electoral Division 2016

Elect_div	Tot_P
Chisholm	498968
Goldstein	432282
Higgins	465317
Hotham	512833
Kooyong	454875
Macnamara	492051
Melbourne	557392

ggplot(Aust_tot_pop,aes(x=Elect_div,y=Tot_P ))+
  geom_bar(stat = 'identity')+
   xlab("Total_Population")+
   ylab("Division Name")+
   ggtitle("Population Distribution Division Wise Chart1")

• Among 7 electoral division, Melborne has highest population of 181375 and Goldstein has the least of 137152.
• Chisholm and Macnamara has almost same population whereas Higgins and Kooyong have similar population with little difference.
• Melbourne’s population is almost 18k more than that of 2nd most populated division i.e. Hotham.

Research Question 2

Show the age distribution (omitting those 80 years old or greater) of each electoral division of interest by plotting a barplot like below. State three interesting observations regarding the plot below with a possibly reason why you see such observations using knowledge about the electoral division (if you do not know, search the internet to find out).

census_path <- here::here("data/2016 Census GCP All Geographies for VIC/{geo}/VIC")

SA1_paths <- glue::glue(census_path, "/2016Census_G04{alpha}_VIC_SA1.csv", 
                        geo = "SA1", alpha = c("A", "B"))

##Cleaning Function

clean_G04P2 <- function(df, id) {
   df %>% 
      pivot_longer(-{{id}},
                   names_to = "category",
                   values_to = "count") %>% 
      filter(str_detect(category, "P")& !str_detect(category,"Tot") & !str_detect(category, "0_4") & !str_detect(category, "5_9") & !str_detect(category, "10_14") & !str_detect(category, "15_19") & !str_detect(category, "20_24") &  !str_detect(category, "25_29") & !str_detect(category, "30_34") & !str_detect(category, "35_39") & !str_detect(category, "40_44") & !str_detect(category, "45_49") & !str_detect(category, "50_54") & !str_detect(category, "55_59") & !str_detect(category, "60_64") &!str_detect(category, "65_69") & !str_detect(category, "70_74") & !str_detect(category, "75_79" & !str_detect(category, "80_84") & !str_detect(category, "85_89") & !str_detect(category, "90_94")& !str_detect(category, "95_99") & !str_detect(category, "100"))%>%
      unglue_unnest(category, 
                   c("{}_{}_{Age=\\d+}_{}"), 
                   remove = FALSE)
}

##Data Cleansing

SA1_G04tidyP <- map_dfr(SA1_paths, ~{
                              df <- read.csv(.x)
                              clean_G04P1(df, SA1_7DIGITCODE_2016)
                           })

SA1_G04tidyP3<-SA1_G04tidyp%>% filter(as.integer(Age)<80)%>% select(SA1_7DIGITCODE_2016,count,Age)

SA1_G04tidyp4<- left_join(sa1_digitcode1,SA1_G04tidyP3, by =c("sa1_7digitcode_2016"="SA1_7DIGITCODE_2016"))%>% select(sa1_7digitcode_2016,Elect_div,count,Age)

write_csv(SA1_G04tidyp4,"data/SA1_G04tidyp4.csv")

SA1_G04tidyp4<-read_csv("data/SA1_G04tidyp4.csv")

ggplot(SA1_G04tidyp4,aes(x=as.numeric(Age),y=(count/sum(count))*600 ))+
  geom_bar(stat = 'identity')+
    facet_grid(Elect_div~ ., scales = 'free')+theme(strip.placement = 'outside', text = element_text(size = 8, lineheight = 2))+
   xlab("Age")+
   ylab("Percentage")+
   ggtitle("Age Distribution Division Wise Chart2")

• Melbourne, age distribution of population is different to other divisions. Infants below 10 years are little and the same above 10 is eve less and reducing till 17 to 18 years range, when it takes a sudden spike and rises to its peak range at 23 years to 27 years. Thereafter it falls like right side of a tapered bell curve and post 40 it maintains a low population density which further gets reduced to a very very little population density in the range of 60 years to 80 years. So, the major population distribution is between 17 year to 40 years range.

• Higgins and Macnamara have very similar age distribution of population. Unlike, Melbourne, density below 10 years is low but more than Melbourne and it falls at a slower rate till 16 to 17 years range. Thereafter it starts rising like a tappered bell curve with maximum distribution at early 30s and then slopes down at same rate as that of rise till start of 40s. Thereafter, although the density gets reduced as the age range increases, but the reduction rate is very less, indicating that there are population among aged range as well. Of course, Higgin’s density in this range is more than that of Macnamara.

• The age distribution of population pattern is some what similar in Goldstein and Kooyong. While in Goldstein, the rise is visible from 0 year to 1 year range and takes its peak at 10 to 11 years range and then falls at the same rate till early 30s and again rises at same pace till 42 to 43 years range and again it is tappered down very gently till 78 to 80 years range, where also the division has good population. In case of Kooyong, pattern is similar but the first wave has the peak in the range of 23 to 24 years and falls till 37 to 40 years and the wave rises again with peak in the range of 46 to 48 years. There after the density is tappered down very gently with substantial population in the range of 70 to 80 years.

Explanation

• Melbourne, Higgins, Macnamara have maximum population density in the younger adult range. These divisions have more industries and provide better potentiality and facilities with education , employability etc. The younger generation is naturally attracted and lives in this division in more numbers in comparison to other divisions.
• Divisions like Chisholm, Kooyong maintains good density in infant to childhood range and also adult to old age range, indicating that these divisions are conducive for living in general with little pollution and with better medical facilities etc.

Research Question 3

What are the percentages of Australian citizens for each electoral division of interest? Why do you think the percentage of Australian citizens is lower for Melbourne, Hotham and Macnamara?

library(tidyverse)
library(sf)
vic_map <- read_sf(here::here("data/E_AUGFN3_region.shp")) %>% 
   # to match up with election data
   mutate(DivisionNm = toupper(Elect_div)) 
sa1_geomap <- read_sf(here::here("data/census2016_eiuwa_vic_short.gpkg"), 
                  layer = "census2016_eiuwa_vic_sa1_short") %>%
mutate(centroid = map(geom, st_centroid),
centroid = st_as_sfc(centroid, crs = st_crs(vic_map)),
which = as.integer(st_intersects(centroid, vic_map)),
Elect_div = ifelse(is.na(which),
"None",
vic_map$Elect_div[which])) %>%
select(sa1_7digitcode_2016, Elect_div)

## although coordinates are longitude/latitude, st_intersects assumes that they are planar

sa1_digitcode1<-sa1_geomap%>% filter (Elect_div %in% c('Melbourne','Kooyong','Macnamara','Chisholm','Higgins','Goldstein','Hotham'))%>% mutate(sa1_7digitcode_2016=as.integer(sa1_7digitcode_2016))

sa1_digitcode1<- as_tibble(sa1_digitcode1)

sa1_digitcode1<- sa1_digitcode1%>% select(sa1_7digitcode_2016, Elect_div)

sa1_digitcode1$sa1_7digitcode_2016<-  as.character( sa1_digitcode1$sa1_7digitcode_2016)

census_path <- here::here("data/2016 Census GCP All Geographies for VIC/{geo}/VIC")

SA1_G01paths <- glue::glue(census_path, "/2016Census_G01_VIC_SA1.csv", 
                        geo = "SA1")

SA1_G01tidy <- read_csv(SA1_G01paths)

SA1_G01tidy1<-SA1_G01tidy%>% select(Australian_citizen_P,SA1_7DIGITCODE_2016)

SA1_G01tidy1$SA1_7DIGITCODE_2016<-  as.character( SA1_G01tidy1$SA1_7DIGITCODE_2016)

SA1_G01tidyjoin<- left_join(data.frame(sa1_digitcode1),SA1_G01tidy1,  by =c("sa1_7digitcode_2016"="SA1_7DIGITCODE_2016")) 

SA1_G01tidyjoin<- as_tibble(SA1_G01tidyjoin)


Aust_citizen_p<-SA1_G01tidyjoin%>% group_by(Elect_div)%>% summarise(Tot_Aust_Citizen_P= sum(Australian_citizen_P))

Aust_totandcitizen<- left_join(Aust_tot_pop,Aust_citizen_p)

Aust_totandcitizen<- Aust_totandcitizen%>% mutate(Percentage_aust= (Tot_Aust_Citizen_P/Tot_P)*100)

#Table using Kableextra function

Aust_totandcitizen%>% mutate(Percentage_aust= (Tot_Aust_Citizen_P/Tot_P)*100)%>%kbl(caption="Percentage Of Australian Citizen In Each Electoral Division",table.attr="style='width:70%;'") %>%
  kable_paper("hover",full_width = T,html_font = "Cambria", position= "left" )

Percentage Of Australian Citizen In Each Electoral Division

Elect_div	Tot_P	Tot_Aust_Citizen_P	Percentage_aust
Chisholm	498968	125734	25.19881
Goldstein	432282	123744	28.62576
Higgins	465317	122026	26.22427
Hotham	512833	126983	24.76108
Kooyong	454875	125556	27.60231
Macnamara	492051	118207	24.02332
Melbourne	557392	108357	19.44000

ggplot(Aust_totandcitizen,aes(x=Elect_div,y= Percentage_aust ))+
  geom_bar(stat = 'identity')+
   xlab("Division Name")+
   ylab("Percentage")+
   ggtitle("Percentag Of Australian Citizen Chart3")

Percentage of Adult Australian Citizens in respect of total population in the division is:

• Melbourne- 19.44
• Hotham- 24.76
• Macnamara- 24.02

Probable Reasons:

• These divisions provide more facilities that attract others to migrate there.
• Have more industry for providing easy employment.
• Have more natural resources.
• Have more educational institutes, etc.

Research Question 4

What is an estimate of adult (i.e. aged 18 years old or over) Australian citizens in each electoral division of interest? State your assumptions for your estimate.

election2019<-read_csv("data/elector-count-election-2016.csv")

election2019$`20-24`<- as.character(election2019$`20-24`)

election2019$`25-29`<- as.character(election2019$`25-29`)

election2019<-election2019%>%
pivot_longer(cols = -Indicators,
                   names_to = "Age",
                   values_to = "count")

election2019$count<-as.integer(election2019$count)

Election<- election2019%>% select(Indicators,count)%>%filter(Indicators %in% c('Melbourne','Kooyong','Macnamara','Chisholm','Higgins','Goldstein','Hotham'))%>%group_by(Indicators)%>% summarise(above18_aust_pop= sum(count))


Election1<-Election%>% mutate(Elect_div= ifelse(Indicators =='MELBOURNE','Melbourne',ifelse( Indicators=='KOOYONG', 'Kooyong',if_else(Indicators =='MACNAMARA' , 'Macnamara', ifelse(Indicators=='CHISHOLM', 'Chisholm',ifelse(Indicators=='HIGGINS', 'Higgins', ifelse(Indicators=='GOLDSTEIN','Goldstein',ifelse( Indicators =='HOTHAM','Hotham',Indicators))))))))

Aust_18plus_pop<-SA1_G04tidyp4%>%group_by(Elect_div)%>% summarise(Tot_18plus_pop= sum(count))

Election1$Elect_div<- as.character(Election1$Elect_div)


##Joining table Election 2016 data and Total Population Data
Aust_18plus_citi_totp<- left_join(Aust_tot_pop,Election1)

Aust_18plus_citi_totp<- Aust_18plus_citi_totp%>%mutate(percentage_18_above= (Aust_18plus_citi_totp$above18_aust_pop/Aust_18plus_citi_totp$Tot_P)*100)

Aust_18plus_citi_totp<- Aust_18plus_citi_totp%>% select(- Indicators)

ggplot(Aust_18plus_citi_totp,aes(x=Elect_div, y= percentage_18_above))+
   geom_bar(stat = 'identity')+
   xlab("Division Name")+
   ylab("Percentage")+
   ggtitle("Percentage of australian citizen 18 years and above  Chart4")

#Table using Kableextra function

Aust_18plus_citi_totp%>%mutate(percentage_18_above= (Aust_18plus_citi_totp$above18_aust_pop/Aust_18plus_citi_totp$Tot_P)*100)%>%kbl(caption="Percentage of australian citizen 18 years and above",table.attr="style='width:70%;'") %>%
  kable_paper("hover",full_width = T,html_font = "Cambria", position= "left" )

Percentage of australian citizen 18 years and above

Elect_div	Tot_P	above18_aust_pop	percentage_18_above
Chisholm	498968	97434	19.52710
Goldstein	432282	104931	24.27374
Higgins	465317	104615	22.48252
Hotham	512833	100010	19.50148
Kooyong	454875	100108	22.00780
Macnamara	492051	NA	NA
Melbourne	557392	111628	20.02684

Estimate of adult (18 years or more) Australian citizens in respect of total population with age 18 and above in the electoral divisions:

• Chisholm: 97434
• Goldstein: 104931
• Higgins: 104615
• Hotham: 100010
• Kooyong: 100108
• Macnamara (as per below estimation) : 104822
• Melbourne: 111628

Assumptions for the estimate for Macnamara:

From table data availed the following details:

• Total population 492051
• Average/Mean of the percentages of 18 years and above (Australian citizen) for divisions excluding Macnamara coming as, 127.82/6= 21.30
• So, 18 and above Australian citizens estimated as 492051X.21.30X.01= 104822

Research Question 5

The political members would like to know the composition of ethnic background of their constituents. Show the top 10 reported ancestry for each electoral division of interest with an appropriate graph. State one interesting observation from your graph.

##clean function

clean_G08 <- function(df, id) {
   df %>% 
      pivot_longer(-{{id}},
                   names_to = "category",
                   values_to = "count") %>% filter(str_detect(category,"Resp"))
}

census_path <- here::here("data/2016 Census GCP All Geographies for VIC/{geo}/VIC")

SA1_G08paths <- glue::glue(census_path, "/2016Census_G08_VIC_SA1.csv", 
                        geo = "SA1")

#Data cleaning

SA1_G08tidy <- map_dfr(SA1_G08paths, ~{
                              df <- read.csv(.x)
                              clean_G08(df, SA1_7DIGITCODE_2016)
})

SA1_G08tidy<-SA1_G08tidy %>% separate(category, c("Ethnicity", "Category"), "_")

SA1_G08tidy1<-SA1_G08tidy %>% mutate(Ethnicity = case_when(Category == "Abor"  ~ "Aust_Abor",Category != "Abor" ~ Ethnicity))

SA1_G08tidy1$SA1_7DIGITCODE_2016<- as.character(SA1_G08tidy1$SA1_7DIGITCODE_2016)

sa1_digitcode1<- sa1_digitcode1%>% select(sa1_7digitcode_2016, Elect_div)
sa1_digitcode1$sa1_7digitcode_2016<- as.character(sa1_digitcode1$sa1_7digitcode_2016)
SA1_G08tidyjoin<- left_join(sa1_digitcode1,SA1_G08tidy1, by =c("sa1_7digitcode_2016"="SA1_7DIGITCODE_2016"))%>% select(sa1_7digitcode_2016,Elect_div,count,Ethnicity)

SA1_G08tidyjoin<- as_tibble(SA1_G08tidyjoin)

SA1_G08tidyjoin<- SA1_G08tidyjoin%>% filter(! SA1_G08tidyjoin$Ethnicity %in% c( "Tot", "Ancestry" ))

ggplot(SA1_G08tidyjoin, aes(y= (count/sum(count))*100 ,x= Ethnicity, fill= Ethnicity))+
   geom_bar(stat = 'identity')+
   scale_color_discrete(name = "Ethnicity")+
   facet_grid(Elect_div ~.)+
   theme(strip.placement = 'outside', text = element_text(size = 8, lineheight = 2))+
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
   ylab("Percentage")+
   xlab("Ethnicity")+
   ggtitle("Composition of ethnic background  Chart5")

SA1_G08tidyjoin%>% arrange(- count) %>% group_by(Elect_div,Ethnicity)%>%summarise(count= sum(count))%>% select(Ethnicity, count)%>% top_n(n=10, wt= count )%>%
ggplot(aes(x= (count/sum(count))*100, y= reorder(Ethnicity,count), fill= Ethnicity))+
   geom_bar(stat = 'identity')+
   facet_wrap(~ Elect_div, scales = 'free')+
   theme(strip.placement = 'outside', text = element_text(size = 8, lineheight = 2))+
   theme(axis.text.x = element_text( vjust = 0.5))+
   xlab("Percentage")+
   ylab("Ethnicity")+
   ggtitle("Top 10 ethnic backgrounds  Chart6")

• Australian Aborigins are missing in the demography in all the divisions.
• In most divisions English community has the highest presence among others except in Cheksholm and Hotham where Chineese presence is highest.
• Although Australians have subatantial presence in all divisions, but could not become as the highest presence yet.

Research Question 6

What are the distribution of religious background in each electoral district of interest? Show this by using a plot. Report one interesting observation from what you see.

library(sf)
library(rgdal)

## Loading required package: sp

## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: D:/New folder (4)/R-4.0.4/library/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: D:/New folder (4)/R-4.0.4/library/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## Overwritten PROJ_LIB was D:/New folder (4)/R-4.0.4/library/rgdal/proj

library(tidyverse)



cldh <- read_sf("data/census2016_cldh_vic_short.gpkg")

cldh1<- cldh%>% select(-geom)
cldh1<- as_tibble(cldh1)
cldh1<- cldh1%>% select(-geom)

#Tidy data

cldh2<- cldh1%>% 
      pivot_longer(cols = -ced_code_2016,
                   names_to = "category",
                   values_to = "count") %>% filter(!str_detect(category,"Tot") & !str_detect(category,"One") & !str_detect(category,"SB") & !str_detect(category,"Religious") & !str_detect(category,"M") & !str_detect(category,"F"))%>% 
   separate(category, c("Religion", "category"), "_")
cldh2<- cldh2%>% mutate(Religiontype = case_when(Religion == "Othr" ~ "Other", Religion != "Othr" ~ Religion))

cldh2$Religion1<- paste(cldh2$Religiontype,cldh2$category)

cldh2<- cldh2%>% mutate(Religion1 = ifelse(Religion1 == "Other Rel", "Other", ifelse(Religion1 == "Other Reln","Other",Religion1)))

library(tidyverse)
library(sf)
vic_map <- read_sf(here::here("data/E_AUGFN3_region.shp")) %>% 
   # to match up with election data
   mutate(DivisionNm = toupper(Elect_div)) 
spc1_geomap <- read_sf("data/census2016_spca_vic_short.gpkg") %>%
mutate(centroid = map(geom, st_centroid),
centroid = st_as_sfc(centroid, crs = st_crs(vic_map)),
which = as.integer(st_intersects(centroid, vic_map)),
Elect_div = ifelse(is.na(which),
"None",
vic_map$Elect_div[which])) %>%
select(ced_code_2016, Elect_div)

## although coordinates are longitude/latitude, st_intersects assumes that they are planar

spc_geomap1<- as_tibble(spc1_geomap)

spc_geomap1<-spc_geomap1%>% select(ced_code_2016,Elect_div)

spc_geomap1<-spc_geomap1%>% mutate(ced_code_2016= as.character(ced_code_2016), Elect_div= as.character(Elect_div))

cldh3_join<- left_join(spc_geomap1,cldh2, by = c("ced_code_2016"="ced_code_2016"))

#Table join using geopack data to get division name of CED_code_2016

cldh3_join%>% filter (Elect_div %in% c('Melbourne','Kooyong','Macnamara','Chisholm','Higgins','Goldstein','Hotham'))%>%
   ggplot(aes(x=Religion1, y=count, fill= Religion1))+
   geom_bar(stat = 'identity')+
   
   facet_grid(Elect_div ~., scales = 'free')+
   theme(strip.placement = 'outside', text = element_text(size = 8, lineheight = 2))+
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
   ggtitle("Distribution of religious background/n in each electoral district of interest Chart7")+
   xlab("Religion")+
   theme(legend.title = element_text("Qualification"))

Christianity as a whole ( 18 sects taking together ) constitute the major religion with almost all of the population following Christianity. Among all types of Christianity, Catholic sects has absolute majority above 90%. Among non-Christian religion, presence of Buddhism, Hinduism, Islam and Judaism can be seen. There are some other religions with small section of people ( termed together as Others )

Research Question 7

What are the level of the highest qualification the person has completed for each electoral division of interest? State one interesting observation with a possible explanation of that observation.

library(sf)
library(rgdal)
library(tidyverse)

EQ <- read_sf("data/census2016_eqa_vic_short.gpkg")

EQ<- as_tibble(EQ)
EQ1<- EQ %>% select(-geom)

EQ2<- EQ1%>% 
      pivot_longer(cols = -ced_code_2016,
                   names_to = "category",
                   values_to = "count") %>% filter( !str_detect(category,"One") & !str_detect(category,"SB") & !str_detect(category,"Religious") & !str_detect(category,"M") & !str_detect(category,"F"))

EQ2_join<- left_join(spc_geomap1,EQ2, by = c("ced_code_2016"="ced_code_2016"))

EQ2_join<-EQ2_join%>% separate(category, c("Qualification","category1","category2"), "_")

EQ2_join1<- EQ2_join%>% mutate(Qualification2= ifelse(Qualification == "P", category1,ifelse(Qualification== "Othr","Other",ifelse(Qualification== "Secondary","Secondary school", ifelse(Qualification== "Pre","Pre-School",ifelse(Qualification== "Infnts","Infants",ifelse(Qualification== "Cert","Cert123",Qualification)))))))%>%filter (Elect_div %in% c('Melbourne','Kooyong','Macnamara','Chisholm','Higgins','Goldstein','Hotham'),Qualification2!="P", Qualification2!= "lfs", Qualification2!= "Tot", Qualification2!="Type", Qualification2!="15")

ggplot(EQ2_join1,aes(x= Qualification2, y= (count/sum(count))*100, fill= Qualification2))+
   geom_bar(stat = 'identity')+
   facet_grid(Elect_div ~., scales = 'free')+
   theme(strip.placement = 'outside', text = element_text(size = 8, lineheight = 2))+
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
   xlab("Qualification")+
   ylab("Percentage")+
   ggtitle("Qualification Distribution Division wise  Chart8")

All the divisions have population with Post Graduation as qualification and simultaneously have population with only Pre-School level qualification. But persons having qualification with Y12e code is maximum in all divisions and comprises more than 80 percent of population in each divisions.

Explanations Post-grad is generally considered as higher rated among qualification and requires highly qualified faculties and related infra-structure. But in all divisions, persons with post-grad qualification are present, ensuring the availability of educational facilities in all divisions. Also, the percentage of post-grad is almost same for all division except for Hotham where it is little less.

The basic qualification for better employment might be Y12e, so that majority avail this qualification as a need. Basic infra structure across the divisions for providing education is more or less same for upto Y12e level. May be education upto Y12e level is free or very less costly.

Employability very high post Y12e level of qualification. ( N.B.:on searching internet, Y12e qualification detail could not be found)

List of data sources

Question 1 resources - Region: Victoria(General Community Profile) - Data used:2016Census_G04_A_VIC_SA1.csv

Question 2 resources - Region: Victoria(General Community Profile) - Data used:2016Census_G04_A_VIC_SA1.csv

Question 3 resources - Region: Victoria(General Community Profile) - Data used:2016Census_G04_A_VIC_SA1.csv & 2016Census_G01_A_VIC_SA1.csv

Question 4 resources - Region:Australia - Data used: elector-count-election-2016.csv & 2016Census_G04_A_VIC_SA1.csv

Question 5 resources - Region:Victoria - Data used: 2016Census_G08_A_VIC_SA1.csv

Question 6 resources - Region:Victoria - Data used: census2016_cldh_vic_short.gkpg

Question 7 resources - Region:Victoria - Data used: census2016_eqa_vic_short.gkpg

Resources

Links

https://datapacks.censusdata.abs.gov.au/geopackages/

https://datapacks.censusdata.abs.gov.au/datapacks/

https://www.aec.gov.au/enrolling_to_vote/enrolment_stats/elector_count/index.htm

R: A Language and Environment for Statistical Computing R Core Team R Foundation for Statistical Computing https://www.R-project.org/

Wickham et al. (2019) Wickham and Hester (2020) Zhu (2021) Tierney et al. (2020) Wickham (2016)

Tierney, Nicholas, Di Cook, Miles McBain, and Colin Fay. 2020. Naniar: Data Structures, Summaries, and Visualisations for Missing Data. https://CRAN.R-project.org/package=naniar.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.

Wickham, Hadley, and Jim Hester. 2020. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.

Zhu, Hao. 2021. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.